Objective:

To identify protein-altering variants that are missed when genetic variants are interpreted using only reference transcripts.

Approach:

• Data Integration: Integrated long-read RNA sequencing data from multiple human tissue atlases with population variation from the Genome Aggregation Database, disease-associated variants from the Genome-Wide Association Studies Catalog and ClinVar, protein structure prediction, …
• Validation: Validated selected findings using targeted long-read RNA sequencing, proteomics, enzymatic assays, and cell-based experiments.

Key Findings:

• Alternative isoform-specific exons harbored a greater burden of genetic variation than reference exons.
• Approximately 80% of alternative transcripts containing disease-associated variants were not represented in current reference genome annotations.
• Computational analyses prioritized multiple missense variants predicted to alter protein structure or stability in alternative isoforms.

Interpretation:

Evaluating genetic variants within tissue-specific alternative transcript isoforms may identify protein-coding consequences that are overlooked when analyses rely exclusively on reference transcripts.

Limitations:

• Most variant effects were based on computational predictions rather than experimental validation.
• Transcriptomic data were derived primarily from healthy tissues rather than disease-specific samples.
• Analysis focused largely on missense variants, and many alternative transcripts are predicted to be nonfunctional or undergo nonsense-mediated decay.

Conclusion:

Assessment of both common and rare disease-associated variants in the context of isoform-specific effects will help explain genetic contributions to human disease.

Sources:

• Nature Communications